Islamic Azad University North Tehran Branch

About: Islamic Azad University North Tehran Branch is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 868 authors who have published 968 publications receiving 9987 citations.

Studying the Effect of Service Outsourcing on Efficiency

Abstract: After finishing of the age of vertical integration in which all organization have implemented all stages of their activities respecting the supply chain, the age of outsourcing is started now. Nowadays, one of the concerns of the manager is to assess and monitor the outsourced process. These activities are sometimes affected by individual reference and thinking orientations of the expert, which may cause the future activities to recreate. In this research, efficiency improvement in the outsourced services in the south pars special economic zone organization is assessed. The statistical society includes all related managers and employees of the organization in zahedan. Then, 196 persons who have been present during the whole outsourcing process are selected this research is based on the quality sheet which chronbach is equal to. /875. The following items are considered for the efficiency assessment process: strategic management, human resource, cost, speed of services, quality of services, and customer satisfaction. This research show that the result of outsourcing in strategic management and human resource aspect are acceptable but not in cost, speed and quality of services aspects.

Binary Image Classification Through an Optimal Topology for Convolutional Neural Networks

Abstract: Deep learning applications in computer vision have expanded over the past years. Image classification, which is the fundamental of most algorithms in the field, has been of interest to many researchers. Advances in hierarchical feature extractions using convolutional neural networks as one of the deep learning architectures have enabled experts to improve the performance of classification significantly. In this work, an optimal binary classifier to distinguish cat and dog images was explored where various architectures and parameters were employed to achieve the best results. To design our experiment, we considered the architectures with two and three convolutional layers using two input image size when models were trained with and without Dropout against an identical dataset. The analysis demonstrated that an accuracy rate of 99.26% for the testing dataset was achieved from a three-layer model with an input image size of 32x32 with Dropout. The classification report of any models was produced to explore other metrics such as precision, recall, and F1-score, and they were aligned with the accuracy rates as this experiment was a balanced data situation.

Neural Localization of Brand Social Responsibility Using EEG

Abstract: *Corresponding Author: Davoud Sadeh E-mail: d.sadeh@qiau.ac.ir

Portfolio Optimization Based on Semi Variance and Another Perspective of Value at Risk Using NSGA II, MOACO, and MOABC Algorithms

Abstract: This study examines the criterion of value at risk from another perspective and presents a new type of mean-value at Risk model. To solve the portfolio optimization problem in Tehran Stock Exchange, we use NSGA II, MOACO, and MOABC algorithms and then compare the mean-pVaR model with the mean-SV model. Given that, finding the best answer is very important in meta-heuristic methods, we use the concept of dominance in the discussion of multi-objective optimization to find the best answers and show that, at low iterations, the performance of the NSGA II algorithm is better than the MOABC and MOACO algorithms in solving the portfolio optimization problem. As the iteration increases, the performance of the algorithms improves, but the rate of improvement is not the same, in a way, the performance of the MOABC algorithm is better than that of the NSGA II and MOACO algorithms. Then, to compare the performance of the “mean-percentage of Value at Risk” model and the “mean-semi variance” model, we examine both models in the standard mean-variance model and show that the mean-pVaR model, compared to the mean-SV model, Has better performance in stock portfolio optimization.